Abstract
Web services are used as the building blocks for IT applications. There are several competing elements to consider when choosing a reliable Internet company. The goal is to divide potential services into several groups based on end users’ preferences and taking into account each service’s distinctive qualities. Our method will look at service characters in terms of highest quality using the kappa statistics value. The kappa statistic methodology is the most effective machine learning technique for evaluating service quality while taking into account a variety of quality attribute values, commonly known as QoS characteristics. For each and every service, our method calculates the classification accuracy score. The kappa static value (KV), a parametric value obtained using a non-leaner model, is used to evaluate a service’s performance using a logistic regression-based accuracy model. The accuracy of each service’s balance is then assessed.
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Prakash, K., Kalaiarasan (2023). A Novel LRKS-WSQoS Model for Web Service Quality Estimation Using Machine Learning-Based Linear Regression and Kappa Methods. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_46
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DOI: https://doi.org/10.1007/978-981-99-6702-5_46
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